Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection
Résumé
In the last decade, High-Throughput Sequencing (HTS) has revolutionized plant virus discovery and diagnosis. Currently, many bioinformatics pipelines for virus detection are available, making the choice of a suitable one difficult. A robust benchmarking is needed for the unbiased comparison of the pipelines.
We present 7 semi-artificial datasets composed of real RNA-seq datasets from virus-infected plants spiked with artificial virus reads. Each dataset addresses challenges that could prevent virus detection (i.e. low viral concentration, new viral species, non-complete virus genome, etc). We also present 3 real datasets showing a challenging virus composition as well as 8 completely artificial datasets to test haplotype reconstruction software.
With these datasets, we hope to encourage virologists, diagnosticians and bioinformaticians to evaluate and benchmark their pipeline(s).